关键词: 尺度不变特征变换（SIFT）, 随机抽样一致性（RANSAC）, 图像配准, 非下采样轮廓波（NSCT）变换, 特征点

Abstract: In order to solve the problem that the accuracy of image registration is not high due to the large number of iterations of RANSAC algorithm, an improved RANSAC image registration algorithm is proposed. First, the reference image and the image to be registered are NSCT transformed into low frequency subband and high frequency subband. Then this paper uses the vector included angle algorithm and Structural Similarity（SSIM） to extract the edge feature points of the high frequency subband, and uses the SIFT algorithm for the low frequency subband and sets the appropriate distance threshold to extract the feature points. Finally, the improved RANSAC algorithm is used to improve the matching of feature points, and the matching points are selected to achieve image registration. The experimental results show that the proposed algorithm can effectively find more pairs of matching points and accurately remove false matching points, which obviously improves the registration accuracy.

Key words: Scale-Invariant Feature Transform（SIFT）, Random Sample Consensus（RANSAC）, image registration, Nonsubsampled Contourlet（NSCT） transformation, feature point